Information Frictions and Behavior in Green Markets

The PhD dissertation Information Frictions and Behavior in Green Markets, investigates how information frictions and behavioral biases shape decision-making and economic outcomes in markets where environmental and moral dimensions are central. Motivated by the growing relevance of sustainability and transparency, the dissertation examines why asymmetric information continues to undermine market efficiency despite decades of theoretical and empirical research, and how behavioral mechanisms and policy interventions can help mitigate these distortions. The thesis adopts both a household and a corporate perspective. On the demand side, it explores whether market inefficiencies stem from unawareness, inattention, or biased decision-making by consumers. On the supply side, it analyzes how firms’ incentives related to image, revenues, and limited monitoring contribute to strategic misrepresentation in environments characterized by informational opacity. The dissertation consists of three chapters. The first chapter, Voluntary Disclosure Under Bounded Rationality: Experimental Evidence (co-authored with Sébastien Houde, University of Lausanne), studies the strategic foundations of voluntary information disclosure. Using a sender–receiver experiment framed in the housing market, it tests whether bounded rationality distorts communication under asymmetric information relative to the benchmark of full unraveling (Milgrom, 1981). The results show that treatment variations significantly affect strategic reasoning and belief formation for both informed and uninformed agents, providing clear evidence of bounded rationality. While full unraveling represents an optimal benchmark in theory, it is rarely observed in practice, as informed agents often benefit from withholding information when receivers anchor their beliefs around average outcomes. The second chapter, Walk the Talk? Greenwashing in the Electricity Market (co-authored with Stefano Verde, University of Siena), introduces a novel methodology to measure greenwashing in the electricity sector. By combining numerical indicators with text-based analysis, the chapter documents substantial heterogeneity in firms’ greenwashing behavior and identifies firm size, profitability, and sectoral characteristics as key correlates of misleading environmental communication. The chapter contributes to the literature by providing a new framework to quantify and analyze greenwashing using integrated quantitative and textual data. The third chapter, Estimating Residential Electricity Demand under Time-of-Use Tariffs: Evidence from Micro-Level Data, provides new evidence on household electricity demand when price information is complex and not easily accessible. Using a unique panel dataset from Estra S.p.A.—the co-funder of the PhD scholarship—the chapter estimates short-run demand elasticities, substitution patterns across peak and off-peak hours, and heterogeneity in consumption behavior across demographic and geographic dimensions. Across chapters, the dissertation combines experimental and behavioral methods, panel data econometrics, and text-mining techniques based on Natural Language Processing (NLP). The central message is that information must be accessible, verifiable, and clearly communicated to all market participants. When these conditions are not met, cognitive biases, strategic behavior, and limited awareness can severely distort economic decisions. The dissertation therefore highlights the importance of monitoring firms’ environmental communications and designing targeted behavioral and informational policies that enable households to recognize how individual actions generate environmental and economic externalities shaping the sustainability of the broader community.